LiDAR-Camera Calibration via Neural Network Alignment Index
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Solution Overview
Problem
Conventional camera and LiDAR sensor systems often suffer from misalignment and calibration errors, leading to inaccurate data, which is critical for autonomous vehicles relying on precise 3D object recognition and navigation.
Innovation Solution
The use of machine-learning models, specifically neural networks, to identify and classify objects in images and LiDAR data, generating an alignment index to determine the calibration status of camera and LiDAR sensors, thereby identifying and correcting misalignment or calibration errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera and LiDAR sensor systems are used for object recognition, then object detection capability is provided, but calibration errors and misalignment occur leading to inaccurate data
Solution Approach 1:
The system uses machine learning models to generate an alignment index that provides feedback on the calibration status of camera and LiDAR sensors. This feedback mechanism enables continuous monitoring and identification of calibration errors, allowing the system to maintain measurement precision and data accuracy by detecting misalignment between sensors.
Solution Approach 2:
The patent replaces traditional mechanical calibration methods with machine learning-based alignment assessment. Instead of relying on manual or hardware-based calibration procedures, the system uses neural networks to analyze sensor data and determine calibration status, thereby eliminating mechanical sources of error and improving both measurement precision and reliability.
2Measurement precision
If machine-learning models are used to identify and correct calibration errors, then calibration accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that mediate between raw sensor data and calibration assessment. These models serve as a bridge, translating complex sensor measurements into interpretable alignment indices, thereby improving calibration accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning models automatically assess and identify calibration errors without requiring external intervention. The alignment index generation and error identification processes are performed autonomously by the trained models, reducing the need for manual calibration procedures and simplifying system operation despite the underlying complexity.
Data Source
AI summary
The subject disclosure relates to techniques for selecting points of an image for processing with LiDAR data. A process of the disclosed technology can include steps receiving an image data comprising an image object comprised of a plurality of image pixels, calculating a number of image pixels corresponding with the image object, receiving Light Detection and Ranging (LiDAR) point cloud data comprising a plurality of LiDAR data points corresponding with the image data collected from the camera sensor, and calculating a number of LiDAR data points corresponding with the image object. Systems and machine-readable media are also provided.